Overview

Dataset statistics

Number of variables27
Number of observations20631
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 MiB
Average record size in memory216.0 B

Variable types

Numeric19
Categorical8

Alerts

Operation Setting 3 has constant value "100.0"Constant
Sensor Measure 1 has constant value "518.67"Constant
Sensor Measure 5 has constant value "14.62"Constant
Sensor Measure 10 has constant value "1.3"Constant
Sensor Measure 16 has constant value "0.03"Constant
Sensor Measure 18 has constant value "2388"Constant
Sensor Measure 19 has constant value "100.0"Constant
Cycle is highly overall correlated with RUL and 10 other fieldsHigh correlation
RUL is highly overall correlated with Cycle and 12 other fieldsHigh correlation
Sensor Measure 11 is highly overall correlated with Cycle and 12 other fieldsHigh correlation
Sensor Measure 12 is highly overall correlated with Cycle and 12 other fieldsHigh correlation
Sensor Measure 13 is highly overall correlated with RUL and 11 other fieldsHigh correlation
Sensor Measure 14 is highly overall correlated with Sensor Measure 9High correlation
Sensor Measure 15 is highly overall correlated with Cycle and 12 other fieldsHigh correlation
Sensor Measure 17 is highly overall correlated with Cycle and 12 other fieldsHigh correlation
Sensor Measure 2 is highly overall correlated with Cycle and 12 other fieldsHigh correlation
Sensor Measure 20 is highly overall correlated with Cycle and 12 other fieldsHigh correlation
Sensor Measure 21 is highly overall correlated with Cycle and 12 other fieldsHigh correlation
Sensor Measure 3 is highly overall correlated with Cycle and 12 other fieldsHigh correlation
Sensor Measure 4 is highly overall correlated with Cycle and 12 other fieldsHigh correlation
Sensor Measure 7 is highly overall correlated with Cycle and 12 other fieldsHigh correlation
Sensor Measure 8 is highly overall correlated with RUL and 11 other fieldsHigh correlation
Sensor Measure 9 is highly overall correlated with Sensor Measure 14High correlation
Sensor Measure 6 is highly imbalanced (86.0%)Imbalance
Operation Setting 1 has 413 (2.0%) zerosZeros
Operation Setting 2 has 2070 (10.0%) zerosZeros

Reproduction

Analysis started2024-06-01 18:38:54.695480
Analysis finished2024-06-01 18:39:47.079751
Duration52.38 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

UnitNumber
Real number (ℝ)

Distinct100
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.506568
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:47.293811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q126
median52
Q377
95-th percentile96
Maximum100
Range99
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.227633
Coefficient of variation (CV)0.56745449
Kurtosis-1.2198241
Mean51.506568
Median Absolute Deviation (MAD)26
Skewness-0.067815234
Sum1062632
Variance854.25453
MonotonicityIncreasing
2024-06-01T20:39:47.471548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 362
 
1.8%
92 341
 
1.7%
96 336
 
1.6%
67 313
 
1.5%
83 293
 
1.4%
2 287
 
1.4%
95 283
 
1.4%
64 283
 
1.4%
86 278
 
1.3%
17 276
 
1.3%
Other values (90) 17579
85.2%
ValueCountFrequency (%)
1 192
0.9%
2 287
1.4%
3 179
0.9%
4 189
0.9%
5 269
1.3%
6 188
0.9%
7 259
1.3%
8 150
0.7%
9 201
1.0%
10 222
1.1%
ValueCountFrequency (%)
100 200
1.0%
99 185
0.9%
98 156
0.8%
97 202
1.0%
96 336
1.6%
95 283
1.4%
94 258
1.3%
93 155
0.8%
92 341
1.7%
91 135
 
0.7%

Cycle
Real number (ℝ)

HIGH CORRELATION 

Distinct362
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.80786
Minimum1
Maximum362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:47.632238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q152
median104
Q3156
95-th percentile230
Maximum362
Range361
Interquartile range (IQR)104

Descriptive statistics

Standard deviation68.88099
Coefficient of variation (CV)0.63305159
Kurtosis-0.2185391
Mean108.80786
Median Absolute Deviation (MAD)52
Skewness0.49990397
Sum2244815
Variance4744.5908
MonotonicityNot monotonic
2024-06-01T20:39:47.795506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 100
 
0.5%
66 100
 
0.5%
97 100
 
0.5%
96 100
 
0.5%
95 100
 
0.5%
94 100
 
0.5%
93 100
 
0.5%
91 100
 
0.5%
90 100
 
0.5%
89 100
 
0.5%
Other values (352) 19631
95.2%
ValueCountFrequency (%)
1 100
0.5%
2 100
0.5%
3 100
0.5%
4 100
0.5%
5 100
0.5%
6 100
0.5%
7 100
0.5%
8 100
0.5%
9 100
0.5%
10 100
0.5%
ValueCountFrequency (%)
362 1
< 0.1%
361 1
< 0.1%
360 1
< 0.1%
359 1
< 0.1%
358 1
< 0.1%
357 1
< 0.1%
356 1
< 0.1%
355 1
< 0.1%
354 1
< 0.1%
353 1
< 0.1%

Operation Setting 1
Real number (ℝ)

ZEROS 

Distinct158
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.8701469 × 10-6
Minimum-0.0087
Maximum0.0087
Zeros413
Zeros (%)2.0%
Negative10061
Negative (%)48.8%
Memory size161.3 KiB
2024-06-01T20:39:47.965563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0087
5-th percentile-0.0037
Q1-0.0015
median0
Q30.0015
95-th percentile0.0036
Maximum0.0087
Range0.0174
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.0021873134
Coefficient of variation (CV)-246.5927
Kurtosis-0.0091316243
Mean-8.8701469 × 10-6
Median Absolute Deviation (MAD)0.0015
Skewness-0.024766267
Sum-0.183
Variance4.7843401 × 10-6
MonotonicityNot monotonic
2024-06-01T20:39:48.109944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 413
 
2.0%
0.0002 398
 
1.9%
0.0004 394
 
1.9%
-0.0005 390
 
1.9%
0.0001 382
 
1.9%
0.0005 381
 
1.8%
0.0006 379
 
1.8%
-0.0006 375
 
1.8%
0.0003 364
 
1.8%
0.0009 362
 
1.8%
Other values (148) 16793
81.4%
ValueCountFrequency (%)
-0.0087 1
 
< 0.1%
-0.0086 1
 
< 0.1%
-0.0084 1
 
< 0.1%
-0.0081 2
< 0.1%
-0.0078 1
 
< 0.1%
-0.0075 1
 
< 0.1%
-0.0074 3
< 0.1%
-0.0073 1
 
< 0.1%
-0.0072 2
< 0.1%
-0.007 2
< 0.1%
ValueCountFrequency (%)
0.0087 1
 
< 0.1%
0.0083 1
 
< 0.1%
0.0077 1
 
< 0.1%
0.0076 1
 
< 0.1%
0.0074 3
< 0.1%
0.0073 1
 
< 0.1%
0.0072 4
< 0.1%
0.0071 2
< 0.1%
0.007 2
< 0.1%
0.0069 2
< 0.1%

Operation Setting 2
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3508313 × 10-6
Minimum-0.0006
Maximum0.0006
Zeros2070
Zeros (%)10.0%
Negative9225
Negative (%)44.7%
Memory size161.3 KiB
2024-06-01T20:39:48.659127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0006
5-th percentile-0.0004
Q1-0.0002
median0
Q30.0003
95-th percentile0.0005
Maximum0.0006
Range0.0012
Interquartile range (IQR)0.0005

Descriptive statistics

Standard deviation0.00029306212
Coefficient of variation (CV)124.66319
Kurtosis-1.130447
Mean2.3508313 × 10-6
Median Absolute Deviation (MAD)0.0003
Skewness0.0090851197
Sum0.0485
Variance8.5885409 × 10-8
MonotonicityNot monotonic
2024-06-01T20:39:48.794713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
-0.0003 2104
10.2%
0.0001 2097
10.2%
0 2070
10.0%
0.0003 2065
10.0%
-0.0004 2051
9.9%
-0.0002 2049
9.9%
0.0002 2038
9.9%
-0.0001 2029
9.8%
0.0004 1997
9.7%
0.0005 1068
5.2%
Other values (3) 1063
5.2%
ValueCountFrequency (%)
-0.0006 34
 
0.2%
-0.0005 958
4.6%
-0.0004 2051
9.9%
-0.0003 2104
10.2%
-0.0002 2049
9.9%
-0.0001 2029
9.8%
0 2070
10.0%
0.0001 2097
10.2%
0.0002 2038
9.9%
0.0003 2065
10.0%
ValueCountFrequency (%)
0.0006 71
 
0.3%
0.0005 1068
5.2%
0.0004 1997
9.7%
0.0003 2065
10.0%
0.0002 2038
9.9%
0.0001 2097
10.2%
0 2070
10.0%
-0.0001 2029
9.8%
-0.0002 2049
9.9%
-0.0003 2104
10.2%

Operation Setting 3
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
100.0
20631 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters103155
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row100.0

Common Values

ValueCountFrequency (%)
100.0 20631
100.0%

Length

2024-06-01T20:39:48.962585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:39:49.072897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
100.0 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103155
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103155
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103155
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

Sensor Measure 1
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
518.67
20631 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters123786
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row518.67
2nd row518.67
3rd row518.67
4th row518.67
5th row518.67

Common Values

ValueCountFrequency (%)
518.67 20631
100.0%

Length

2024-06-01T20:39:49.171861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:39:49.278306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
518.67 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
5 20631
16.7%
1 20631
16.7%
8 20631
16.7%
. 20631
16.7%
6 20631
16.7%
7 20631
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 123786
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 20631
16.7%
1 20631
16.7%
8 20631
16.7%
. 20631
16.7%
6 20631
16.7%
7 20631
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 123786
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 20631
16.7%
1 20631
16.7%
8 20631
16.7%
. 20631
16.7%
6 20631
16.7%
7 20631
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 123786
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 20631
16.7%
1 20631
16.7%
8 20631
16.7%
. 20631
16.7%
6 20631
16.7%
7 20631
16.7%

Sensor Measure 2
Real number (ℝ)

HIGH CORRELATION 

Distinct310
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean642.68093
Minimum641.21
Maximum644.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:49.435902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum641.21
5-th percentile641.92
Q1642.325
median642.64
Q3643
95-th percentile643.58
Maximum644.53
Range3.32
Interquartile range (IQR)0.675

Descriptive statistics

Standard deviation0.50005327
Coefficient of variation (CV)0.00077807392
Kurtosis-0.11204294
Mean642.68093
Median Absolute Deviation (MAD)0.34
Skewness0.31652589
Sum13259150
Variance0.25005327
MonotonicityNot monotonic
2024-06-01T20:39:49.645933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
642.5 190
 
0.9%
642.56 189
 
0.9%
642.53 188
 
0.9%
642.6 184
 
0.9%
642.67 179
 
0.9%
642.44 175
 
0.8%
642.63 175
 
0.8%
642.57 172
 
0.8%
642.64 168
 
0.8%
642.73 167
 
0.8%
Other values (300) 18844
91.3%
ValueCountFrequency (%)
641.21 1
 
< 0.1%
641.25 2
< 0.1%
641.27 3
< 0.1%
641.3 4
< 0.1%
641.31 1
 
< 0.1%
641.32 2
< 0.1%
641.33 2
< 0.1%
641.34 1
 
< 0.1%
641.35 1
 
< 0.1%
641.36 2
< 0.1%
ValueCountFrequency (%)
644.53 2
< 0.1%
644.5 1
< 0.1%
644.47 1
< 0.1%
644.44 1
< 0.1%
644.39 1
< 0.1%
644.37 1
< 0.1%
644.35 1
< 0.1%
644.34 1
< 0.1%
644.31 1
< 0.1%
644.3 2
< 0.1%

Sensor Measure 3
Real number (ℝ)

HIGH CORRELATION 

Distinct3012
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1590.5231
Minimum1571.04
Maximum1616.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:49.808510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1571.04
5-th percentile1581.11
Q11586.26
median1590.1
Q31594.38
95-th percentile1601.47
Maximum1616.91
Range45.87
Interquartile range (IQR)8.12

Descriptive statistics

Standard deviation6.1311495
Coefficient of variation (CV)0.0038548006
Kurtosis0.0077618224
Mean1590.5231
Median Absolute Deviation (MAD)4.05
Skewness0.30894581
Sum32814082
Variance37.590994
MonotonicityNot monotonic
2024-06-01T20:39:49.978681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1590.1 27
 
0.1%
1589.76 26
 
0.1%
1589.98 25
 
0.1%
1592.11 25
 
0.1%
1587.86 24
 
0.1%
1584.95 23
 
0.1%
1590.54 23
 
0.1%
1589.08 23
 
0.1%
1589.44 23
 
0.1%
1587.82 22
 
0.1%
Other values (3002) 20390
98.8%
ValueCountFrequency (%)
1571.04 1
< 0.1%
1571.06 1
< 0.1%
1571.84 1
< 0.1%
1571.99 1
< 0.1%
1572.34 1
< 0.1%
1572.4 1
< 0.1%
1572.46 1
< 0.1%
1572.67 1
< 0.1%
1572.76 1
< 0.1%
1572.98 1
< 0.1%
ValueCountFrequency (%)
1616.91 1
< 0.1%
1614.93 1
< 0.1%
1614.72 1
< 0.1%
1613.62 1
< 0.1%
1613.29 1
< 0.1%
1612.88 1
< 0.1%
1612.63 1
< 0.1%
1612.11 1
< 0.1%
1611.92 1
< 0.1%
1611.57 1
< 0.1%

Sensor Measure 4
Real number (ℝ)

HIGH CORRELATION 

Distinct4051
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1408.9338
Minimum1382.25
Maximum1441.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:50.155426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1382.25
5-th percentile1395.62
Q11402.36
median1408.04
Q31414.555
95-th percentile1425.67
Maximum1441.49
Range59.24
Interquartile range (IQR)12.195

Descriptive statistics

Standard deviation9.0006048
Coefficient of variation (CV)0.0063882383
Kurtosis-0.16368086
Mean1408.9338
Median Absolute Deviation (MAD)6.04
Skewness0.44319434
Sum29067713
Variance81.010886
MonotonicityNot monotonic
2024-06-01T20:39:50.381994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1409.01 20
 
0.1%
1404.47 18
 
0.1%
1407.15 18
 
0.1%
1407.02 18
 
0.1%
1414.03 18
 
0.1%
1410.54 18
 
0.1%
1403.23 17
 
0.1%
1407.18 16
 
0.1%
1410.57 16
 
0.1%
1401.27 16
 
0.1%
Other values (4041) 20456
99.2%
ValueCountFrequency (%)
1382.25 1
< 0.1%
1385.19 1
< 0.1%
1385.75 1
< 0.1%
1386.29 1
< 0.1%
1386.43 1
< 0.1%
1386.69 1
< 0.1%
1387.16 1
< 0.1%
1387.36 1
< 0.1%
1387.38 1
< 0.1%
1387.5 1
< 0.1%
ValueCountFrequency (%)
1441.49 1
< 0.1%
1438.96 1
< 0.1%
1438.51 1
< 0.1%
1438.41 1
< 0.1%
1438.22 1
< 0.1%
1438.16 1
< 0.1%
1438.1 1
< 0.1%
1437.98 1
< 0.1%
1437.88 1
< 0.1%
1437.81 1
< 0.1%

Sensor Measure 5
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
14.62
20631 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters103155
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14.62
2nd row14.62
3rd row14.62
4th row14.62
5th row14.62

Common Values

ValueCountFrequency (%)
14.62 20631
100.0%

Length

2024-06-01T20:39:50.550892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:39:50.655737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
14.62 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
1 20631
20.0%
4 20631
20.0%
. 20631
20.0%
6 20631
20.0%
2 20631
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103155
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 20631
20.0%
4 20631
20.0%
. 20631
20.0%
6 20631
20.0%
2 20631
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103155
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 20631
20.0%
4 20631
20.0%
. 20631
20.0%
6 20631
20.0%
2 20631
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103155
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 20631
20.0%
4 20631
20.0%
. 20631
20.0%
6 20631
20.0%
2 20631
20.0%

Sensor Measure 6
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
21.61
20225 
21.6
 
406

Length

Max length5
Median length5
Mean length4.9803209
Min length4

Characters and Unicode

Total characters102749
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21.61
2nd row21.61
3rd row21.61
4th row21.61
5th row21.61

Common Values

ValueCountFrequency (%)
21.61 20225
98.0%
21.6 406
 
2.0%

Length

2024-06-01T20:39:50.774496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:39:50.875654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
21.61 20225
98.0%
21.6 406
 
2.0%

Most occurring characters

ValueCountFrequency (%)
1 40856
39.8%
2 20631
20.1%
. 20631
20.1%
6 20631
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 102749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 40856
39.8%
2 20631
20.1%
. 20631
20.1%
6 20631
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 102749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 40856
39.8%
2 20631
20.1%
. 20631
20.1%
6 20631
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 102749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 40856
39.8%
2 20631
20.1%
. 20631
20.1%
6 20631
20.1%

Sensor Measure 7
Real number (ℝ)

HIGH CORRELATION 

Distinct513
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean553.36771
Minimum549.85
Maximum556.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:51.012665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum549.85
5-th percentile551.74
Q1552.81
median553.44
Q3554.01
95-th percentile554.69
Maximum556.06
Range6.21
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.88509226
Coefficient of variation (CV)0.0015994649
Kurtosis-0.15794922
Mean553.36771
Median Absolute Deviation (MAD)0.6
Skewness-0.39432894
Sum11416529
Variance0.7833883
MonotonicityNot monotonic
2024-06-01T20:39:51.231169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
553.62 116
 
0.6%
553.76 115
 
0.6%
553.72 110
 
0.5%
553.94 110
 
0.5%
553.43 108
 
0.5%
553.74 107
 
0.5%
553.75 106
 
0.5%
554 105
 
0.5%
553.9 104
 
0.5%
553.52 103
 
0.5%
Other values (503) 19547
94.7%
ValueCountFrequency (%)
549.85 1
< 0.1%
550.34 1
< 0.1%
550.35 1
< 0.1%
550.42 1
< 0.1%
550.43 1
< 0.1%
550.48 2
< 0.1%
550.49 1
< 0.1%
550.5 1
< 0.1%
550.51 2
< 0.1%
550.52 1
< 0.1%
ValueCountFrequency (%)
556.06 1
< 0.1%
555.86 1
< 0.1%
555.72 1
< 0.1%
555.7 1
< 0.1%
555.67 1
< 0.1%
555.66 1
< 0.1%
555.64 1
< 0.1%
555.61 1
< 0.1%
555.6 1
< 0.1%
555.58 1
< 0.1%

Sensor Measure 8
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.0967
Minimum2387.9
Maximum2388.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:51.403075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2387.9
5-th percentile2387.99
Q12388.05
median2388.09
Q32388.14
95-th percentile2388.22
Maximum2388.56
Range0.66
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.070985479
Coefficient of variation (CV)2.9724709 × 10-5
Kurtosis0.33314901
Mean2388.0967
Median Absolute Deviation (MAD)0.05
Skewness0.47941086
Sum49268822
Variance0.0050389382
MonotonicityNot monotonic
2024-06-01T20:39:51.567220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2388.11 1181
 
5.7%
2388.1 1159
 
5.6%
2388.09 1149
 
5.6%
2388.08 1126
 
5.5%
2388.07 1077
 
5.2%
2388.12 1069
 
5.2%
2388.06 1050
 
5.1%
2388.13 1033
 
5.0%
2388.05 1013
 
4.9%
2388.04 910
 
4.4%
Other values (43) 9864
47.8%
ValueCountFrequency (%)
2387.9 1
 
< 0.1%
2387.91 3
 
< 0.1%
2387.92 9
 
< 0.1%
2387.93 16
 
0.1%
2387.94 33
 
0.2%
2387.95 72
 
0.3%
2387.96 145
 
0.7%
2387.97 201
1.0%
2387.98 339
1.6%
2387.99 426
2.1%
ValueCountFrequency (%)
2388.56 1
 
< 0.1%
2388.52 1
 
< 0.1%
2388.5 1
 
< 0.1%
2388.46 1
 
< 0.1%
2388.44 2
 
< 0.1%
2388.37 1
 
< 0.1%
2388.36 1
 
< 0.1%
2388.35 2
 
< 0.1%
2388.34 13
0.1%
2388.33 13
0.1%

Sensor Measure 9
Real number (ℝ)

HIGH CORRELATION 

Distinct6403
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9065.2429
Minimum9021.73
Maximum9244.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:51.752945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum9021.73
5-th percentile9042.55
Q19053.1
median9060.66
Q39069.42
95-th percentile9109.98
Maximum9244.59
Range222.86
Interquartile range (IQR)16.32

Descriptive statistics

Standard deviation22.08288
Coefficient of variation (CV)0.0024359942
Kurtosis9.3786813
Mean9065.2429
Median Absolute Deviation (MAD)8.13
Skewness2.5553649
Sum1.8702503 × 108
Variance487.65357
MonotonicityNot monotonic
2024-06-01T20:39:51.929682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9058.88 16
 
0.1%
9060.37 15
 
0.1%
9060.55 15
 
0.1%
9056.86 15
 
0.1%
9063.22 15
 
0.1%
9060.87 15
 
0.1%
9054.54 14
 
0.1%
9061.05 14
 
0.1%
9057.95 14
 
0.1%
9065.47 14
 
0.1%
Other values (6393) 20484
99.3%
ValueCountFrequency (%)
9021.73 1
< 0.1%
9023.85 1
< 0.1%
9024.27 1
< 0.1%
9024.42 1
< 0.1%
9025.22 1
< 0.1%
9025.29 1
< 0.1%
9026.08 1
< 0.1%
9026.17 1
< 0.1%
9026.19 1
< 0.1%
9026.66 1
< 0.1%
ValueCountFrequency (%)
9244.59 1
< 0.1%
9239.76 1
< 0.1%
9228.53 1
< 0.1%
9226.6 1
< 0.1%
9224.87 1
< 0.1%
9224.53 1
< 0.1%
9223.56 1
< 0.1%
9221.31 1
< 0.1%
9220.88 1
< 0.1%
9219.81 1
< 0.1%

Sensor Measure 10
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1.3
20631 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters61893
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.3
2nd row1.3
3rd row1.3
4th row1.3
5th row1.3

Common Values

ValueCountFrequency (%)
1.3 20631
100.0%

Length

2024-06-01T20:39:52.067479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:39:52.164434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1.3 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
1 20631
33.3%
. 20631
33.3%
3 20631
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61893
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 20631
33.3%
. 20631
33.3%
3 20631
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61893
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 20631
33.3%
. 20631
33.3%
3 20631
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61893
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 20631
33.3%
. 20631
33.3%
3 20631
33.3%

Sensor Measure 11
Real number (ℝ)

HIGH CORRELATION 

Distinct159
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.541168
Minimum46.85
Maximum48.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:52.300521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum46.85
5-th percentile47.15
Q147.35
median47.51
Q347.7
95-th percentile48.045
Maximum48.53
Range1.68
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.2670874
Coefficient of variation (CV)0.0056180235
Kurtosis-0.17219188
Mean47.541168
Median Absolute Deviation (MAD)0.18
Skewness0.46932909
Sum980821.84
Variance0.071335679
MonotonicityNot monotonic
2024-06-01T20:39:52.503549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.46 341
 
1.7%
47.57 338
 
1.6%
47.49 332
 
1.6%
47.45 332
 
1.6%
47.47 331
 
1.6%
47.52 326
 
1.6%
47.37 321
 
1.6%
47.48 319
 
1.5%
47.44 318
 
1.5%
47.43 311
 
1.5%
Other values (149) 17362
84.2%
ValueCountFrequency (%)
46.85 1
 
< 0.1%
46.86 3
< 0.1%
46.88 2
 
< 0.1%
46.89 1
 
< 0.1%
46.9 1
 
< 0.1%
46.91 1
 
< 0.1%
46.92 3
< 0.1%
46.93 3
< 0.1%
46.94 6
< 0.1%
46.95 6
< 0.1%
ValueCountFrequency (%)
48.53 1
 
< 0.1%
48.52 1
 
< 0.1%
48.48 1
 
< 0.1%
48.43 1
 
< 0.1%
48.41 4
< 0.1%
48.4 4
< 0.1%
48.39 3
< 0.1%
48.38 1
 
< 0.1%
48.37 2
 
< 0.1%
48.35 5
< 0.1%

Sensor Measure 12
Real number (ℝ)

HIGH CORRELATION 

Distinct427
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521.41347
Minimum518.69
Maximum523.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:52.672232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum518.69
5-th percentile520.04
Q1520.96
median521.48
Q3521.95
95-th percentile522.5
Maximum523.38
Range4.69
Interquartile range (IQR)0.99

Descriptive statistics

Standard deviation0.73755339
Coefficient of variation (CV)0.0014145269
Kurtosis-0.14491657
Mean521.41347
Median Absolute Deviation (MAD)0.5
Skewness-0.44240724
Sum10757281
Variance0.54398501
MonotonicityNot monotonic
2024-06-01T20:39:52.818004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521.63 143
 
0.7%
521.42 136
 
0.7%
521.35 131
 
0.6%
521.56 129
 
0.6%
521.66 126
 
0.6%
521.54 125
 
0.6%
521.69 124
 
0.6%
521.5 123
 
0.6%
521.46 121
 
0.6%
521.43 121
 
0.6%
Other values (417) 19352
93.8%
ValueCountFrequency (%)
518.69 1
< 0.1%
518.83 2
< 0.1%
518.94 1
< 0.1%
518.95 1
< 0.1%
518.98 1
< 0.1%
518.99 1
< 0.1%
519.01 1
< 0.1%
519.02 1
< 0.1%
519.03 1
< 0.1%
519.06 2
< 0.1%
ValueCountFrequency (%)
523.38 2
< 0.1%
523.35 1
< 0.1%
523.31 1
< 0.1%
523.27 1
< 0.1%
523.26 2
< 0.1%
523.25 1
< 0.1%
523.24 1
< 0.1%
523.23 1
< 0.1%
523.21 1
< 0.1%
523.2 1
< 0.1%

Sensor Measure 13
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.0962
Minimum2387.88
Maximum2388.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:52.981343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2387.88
5-th percentile2387.99
Q12388.04
median2388.09
Q32388.14
95-th percentile2388.23
Maximum2388.56
Range0.68
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.071918916
Coefficient of variation (CV)3.0115586 × 10-5
Kurtosis0.38724376
Mean2388.0962
Median Absolute Deviation (MAD)0.05
Skewness0.46979242
Sum49268812
Variance0.0051723304
MonotonicityNot monotonic
2024-06-01T20:39:53.184042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2388.1 1164
 
5.6%
2388.09 1144
 
5.5%
2388.08 1129
 
5.5%
2388.11 1127
 
5.5%
2388.07 1112
 
5.4%
2388.12 1099
 
5.3%
2388.06 1005
 
4.9%
2388.05 987
 
4.8%
2388.13 976
 
4.7%
2388.04 952
 
4.6%
Other values (46) 9936
48.2%
ValueCountFrequency (%)
2387.88 1
 
< 0.1%
2387.89 1
 
< 0.1%
2387.9 1
 
< 0.1%
2387.91 2
 
< 0.1%
2387.92 12
 
0.1%
2387.93 19
 
0.1%
2387.94 54
 
0.3%
2387.95 95
0.5%
2387.96 170
0.8%
2387.97 219
1.1%
ValueCountFrequency (%)
2388.56 1
 
< 0.1%
2388.55 1
 
< 0.1%
2388.54 1
 
< 0.1%
2388.49 1
 
< 0.1%
2388.44 1
 
< 0.1%
2388.39 2
 
< 0.1%
2388.37 3
 
< 0.1%
2388.36 6
< 0.1%
2388.35 7
< 0.1%
2388.34 8
< 0.1%

Sensor Measure 14
Real number (ℝ)

HIGH CORRELATION 

Distinct6078
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8143.7527
Minimum8099.94
Maximum8293.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:53.343254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum8099.94
5-th percentile8122.505
Q18133.245
median8140.54
Q38148.31
95-th percentile8181.405
Maximum8293.72
Range193.78
Interquartile range (IQR)15.065

Descriptive statistics

Standard deviation19.076176
Coefficient of variation (CV)0.0023424306
Kurtosis8.8546645
Mean8143.7527
Median Absolute Deviation (MAD)7.54
Skewness2.3725536
Sum1.6801376 × 108
Variance363.90049
MonotonicityNot monotonic
2024-06-01T20:39:53.545389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8138.89 17
 
0.1%
8141.85 17
 
0.1%
8136.89 16
 
0.1%
8140.79 15
 
0.1%
8140.65 15
 
0.1%
8140.49 15
 
0.1%
8140.33 15
 
0.1%
8140.89 15
 
0.1%
8136.69 15
 
0.1%
8140.97 15
 
0.1%
Other values (6068) 20476
99.2%
ValueCountFrequency (%)
8099.94 1
< 0.1%
8101.49 1
< 0.1%
8102.82 1
< 0.1%
8103.27 1
< 0.1%
8103.77 1
< 0.1%
8103.98 1
< 0.1%
8104.46 1
< 0.1%
8104.78 1
< 0.1%
8104.82 1
< 0.1%
8105.22 1
< 0.1%
ValueCountFrequency (%)
8293.72 1
< 0.1%
8290.25 1
< 0.1%
8289.63 1
< 0.1%
8288.26 1
< 0.1%
8282.5 1
< 0.1%
8279.86 1
< 0.1%
8279.79 1
< 0.1%
8276.2 1
< 0.1%
8274.65 1
< 0.1%
8273.15 1
< 0.1%

Sensor Measure 15
Real number (ℝ)

HIGH CORRELATION 

Distinct1918
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4421456
Minimum8.3249
Maximum8.5848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:53.706511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum8.3249
5-th percentile8.3859
Q18.4149
median8.4389
Q38.4656
95-th percentile8.511
Maximum8.5848
Range0.2599
Interquartile range (IQR)0.0507

Descriptive statistics

Standard deviation0.037505038
Coefficient of variation (CV)0.0044425955
Kurtosis-0.12143
Mean8.4421456
Median Absolute Deviation (MAD)0.0252
Skewness0.38825858
Sum174169.91
Variance0.0014066279
MonotonicityNot monotonic
2024-06-01T20:39:53.859405image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.4309 38
 
0.2%
8.4318 37
 
0.2%
8.4468 36
 
0.2%
8.4442 35
 
0.2%
8.4128 34
 
0.2%
8.4453 32
 
0.2%
8.4446 32
 
0.2%
8.4371 31
 
0.2%
8.4209 31
 
0.2%
8.4226 31
 
0.2%
Other values (1908) 20294
98.4%
ValueCountFrequency (%)
8.3249 1
< 0.1%
8.3279 1
< 0.1%
8.3303 1
< 0.1%
8.3358 2
< 0.1%
8.3365 1
< 0.1%
8.3387 1
< 0.1%
8.34 1
< 0.1%
8.3409 1
< 0.1%
8.3427 1
< 0.1%
8.3428 1
< 0.1%
ValueCountFrequency (%)
8.5848 1
< 0.1%
8.5836 1
< 0.1%
8.5678 1
< 0.1%
8.5671 1
< 0.1%
8.5668 1
< 0.1%
8.5665 1
< 0.1%
8.5654 1
< 0.1%
8.5648 1
< 0.1%
8.5646 1
< 0.1%
8.5641 1
< 0.1%

Sensor Measure 16
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.03
20631 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters82524
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.03
2nd row0.03
3rd row0.03
4th row0.03
5th row0.03

Common Values

ValueCountFrequency (%)
0.03 20631
100.0%

Length

2024-06-01T20:39:54.009822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:39:54.124345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
0 41262
50.0%
. 20631
25.0%
3 20631
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 82524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 41262
50.0%
. 20631
25.0%
3 20631
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 82524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 41262
50.0%
. 20631
25.0%
3 20631
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 82524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 41262
50.0%
. 20631
25.0%
3 20631
25.0%

Sensor Measure 17
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean393.21065
Minimum388
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:54.228257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum388
5-th percentile391
Q1392
median393
Q3394
95-th percentile396
Maximum400
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.548763
Coefficient of variation (CV)0.0039387616
Kurtosis-0.039174043
Mean393.21065
Median Absolute Deviation (MAD)1
Skewness0.35312566
Sum8112329
Variance2.3986669
MonotonicityNot monotonic
2024-06-01T20:39:54.356587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
393 5445
26.4%
392 4578
22.2%
394 4063
19.7%
395 2339
11.3%
391 2022
 
9.8%
396 1185
 
5.7%
390 452
 
2.2%
397 436
 
2.1%
398 72
 
0.3%
389 30
 
0.1%
Other values (3) 9
 
< 0.1%
ValueCountFrequency (%)
388 1
 
< 0.1%
389 30
 
0.1%
390 452
 
2.2%
391 2022
 
9.8%
392 4578
22.2%
393 5445
26.4%
394 4063
19.7%
395 2339
11.3%
396 1185
 
5.7%
397 436
 
2.1%
ValueCountFrequency (%)
400 1
 
< 0.1%
399 7
 
< 0.1%
398 72
 
0.3%
397 436
 
2.1%
396 1185
 
5.7%
395 2339
11.3%
394 4063
19.7%
393 5445
26.4%
392 4578
22.2%
391 2022
 
9.8%

Sensor Measure 18
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2388
20631 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters82524
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2388
2nd row2388
3rd row2388
4th row2388
5th row2388

Common Values

ValueCountFrequency (%)
2388 20631
100.0%

Length

2024-06-01T20:39:54.498858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:39:54.613789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2388 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
8 41262
50.0%
2 20631
25.0%
3 20631
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 82524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 41262
50.0%
2 20631
25.0%
3 20631
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 82524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 41262
50.0%
2 20631
25.0%
3 20631
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 82524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 41262
50.0%
2 20631
25.0%
3 20631
25.0%

Sensor Measure 19
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
100.0
20631 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters103155
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row100.0

Common Values

ValueCountFrequency (%)
100.0 20631
100.0%

Length

2024-06-01T20:39:54.716567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-01T20:39:54.821236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
100.0 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103155
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103155
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103155
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

Sensor Measure 20
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.816271
Minimum38.14
Maximum39.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:54.949421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum38.14
5-th percentile38.49
Q138.7
median38.83
Q338.95
95-th percentile39.09
Maximum39.43
Range1.29
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.18074643
Coefficient of variation (CV)0.0046564604
Kurtosis-0.11282911
Mean38.816271
Median Absolute Deviation (MAD)0.12
Skewness-0.3584452
Sum800818.48
Variance0.032669271
MonotonicityNot monotonic
2024-06-01T20:39:55.125007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.86 485
 
2.4%
38.89 476
 
2.3%
38.82 472
 
2.3%
38.87 460
 
2.2%
38.85 458
 
2.2%
38.83 457
 
2.2%
38.84 455
 
2.2%
38.88 452
 
2.2%
38.81 447
 
2.2%
38.8 447
 
2.2%
Other values (110) 16022
77.7%
ValueCountFrequency (%)
38.14 1
 
< 0.1%
38.16 1
 
< 0.1%
38.18 1
 
< 0.1%
38.19 1
 
< 0.1%
38.2 1
 
< 0.1%
38.21 1
 
< 0.1%
38.22 3
 
< 0.1%
38.23 5
< 0.1%
38.24 7
< 0.1%
38.25 9
< 0.1%
ValueCountFrequency (%)
39.43 1
 
< 0.1%
39.41 1
 
< 0.1%
39.34 1
 
< 0.1%
39.32 1
 
< 0.1%
39.31 2
 
< 0.1%
39.3 2
 
< 0.1%
39.29 3
 
< 0.1%
39.28 1
 
< 0.1%
39.27 10
< 0.1%
39.26 7
< 0.1%

Sensor Measure 21
Real number (ℝ)

HIGH CORRELATION 

Distinct4745
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.289705
Minimum22.8942
Maximum23.6184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:55.312367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum22.8942
5-th percentile23.09345
Q123.2218
median23.2979
Q323.3668
95-th percentile23.4535
Maximum23.6184
Range0.7242
Interquartile range (IQR)0.145

Descriptive statistics

Standard deviation0.10825087
Coefficient of variation (CV)0.0046480139
Kurtosis-0.11703945
Mean23.289705
Median Absolute Deviation (MAD)0.0724
Skewness-0.35037496
Sum480489.91
Variance0.011718252
MonotonicityNot monotonic
2024-06-01T20:39:55.479237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.3222 23
 
0.1%
23.3029 17
 
0.1%
23.2896 16
 
0.1%
23.3725 16
 
0.1%
23.371 15
 
0.1%
23.3491 15
 
0.1%
23.3497 15
 
0.1%
23.3315 15
 
0.1%
23.3002 15
 
0.1%
23.3309 15
 
0.1%
Other values (4735) 20469
99.2%
ValueCountFrequency (%)
22.8942 1
< 0.1%
22.9071 1
< 0.1%
22.9122 1
< 0.1%
22.9305 1
< 0.1%
22.9333 1
< 0.1%
22.9337 1
< 0.1%
22.9364 1
< 0.1%
22.9396 2
< 0.1%
22.9398 1
< 0.1%
22.9402 1
< 0.1%
ValueCountFrequency (%)
23.6184 1
< 0.1%
23.6127 1
< 0.1%
23.6064 1
< 0.1%
23.6005 1
< 0.1%
23.5983 1
< 0.1%
23.589 1
< 0.1%
23.5862 2
< 0.1%
23.5858 1
< 0.1%
23.5825 1
< 0.1%
23.5791 1
< 0.1%

RUL
Real number (ℝ)

HIGH CORRELATION 

Distinct362
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.80786
Minimum1
Maximum362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2024-06-01T20:39:55.634436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q152
median104
Q3156
95-th percentile230
Maximum362
Range361
Interquartile range (IQR)104

Descriptive statistics

Standard deviation68.88099
Coefficient of variation (CV)0.63305159
Kurtosis-0.2185391
Mean108.80786
Median Absolute Deviation (MAD)52
Skewness0.49990397
Sum2244815
Variance4744.5908
MonotonicityNot monotonic
2024-06-01T20:39:55.804830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 100
 
0.5%
62 100
 
0.5%
71 100
 
0.5%
70 100
 
0.5%
69 100
 
0.5%
68 100
 
0.5%
67 100
 
0.5%
66 100
 
0.5%
65 100
 
0.5%
64 100
 
0.5%
Other values (352) 19631
95.2%
ValueCountFrequency (%)
1 100
0.5%
2 100
0.5%
3 100
0.5%
4 100
0.5%
5 100
0.5%
6 100
0.5%
7 100
0.5%
8 100
0.5%
9 100
0.5%
10 100
0.5%
ValueCountFrequency (%)
362 1
< 0.1%
361 1
< 0.1%
360 1
< 0.1%
359 1
< 0.1%
358 1
< 0.1%
357 1
< 0.1%
356 1
< 0.1%
355 1
< 0.1%
354 1
< 0.1%
353 1
< 0.1%

Interactions

2024-06-01T20:39:43.837084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:38:56.489074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:38:59.081942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:01.599998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:04.178071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:06.767437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:09.162138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:11.796159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:14.295911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:16.950281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:19.838764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:22.501348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:25.019425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:27.683825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:30.302273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:33.125801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:35.918519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:38.906746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:41.373656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:43.962600image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:38:56.618884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:38:59.202326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:01.742431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:04.303236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:06.906527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:09.288167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:11.917818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:14.407966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:17.070956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:19.975215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:22.656703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:25.154629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:27.805267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:30.445971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:33.240219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:36.039898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:39.020728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:41.502428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:44.085683image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:38:56.739892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:38:59.314080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:01.854029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:04.443837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:07.039743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:09.419728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:12.037713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:14.563910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:17.489515image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:20.151035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:22.819531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:25.285051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:27.923781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:30.574533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:33.363238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:36.194630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:39.137149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:41.631289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:44.221543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:38:56.873341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:38:59.421228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:01.993063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:04.621797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:07.160667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:09.565546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:12.150959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:14.722906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:17.624954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:20.328777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:22.933681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:25.426052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:28.060922image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:30.699968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:33.535110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:36.749301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:39.260143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:41.776639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:44.337964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:38:57.040183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:38:59.569505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-06-01T20:39:27.084526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:29.649759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:32.386232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:35.164122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:38.238437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:40.729056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:43.190015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:45.789977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:38:58.585407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:00.968918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:03.633190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:06.243171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:08.650579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:11.296637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:13.765676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:16.363595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:19.281347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:21.974822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:24.508177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:27.190315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:29.773670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:32.559044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:35.313690image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:38.360607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:40.855812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:43.294182image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:45.920435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:38:58.706071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:01.080829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:03.765924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:06.381004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:08.780996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:11.418209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:13.899069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:16.484169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:19.417095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:22.078565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:24.622712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:27.330238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:29.924627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:32.707760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:35.479968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:38.516633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:41.006368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:43.430913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:46.057318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:38:58.818220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:01.227965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:03.899119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:06.512154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:08.910137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:11.539806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:14.039604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:16.687469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:19.544049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:22.208472image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:24.732384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:27.447448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:30.045131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:32.858618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:35.642293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:38.641525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:41.113637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:43.565153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:46.203840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:38:58.949433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:01.487948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:04.052161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:06.629858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:09.027717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:11.656846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:14.186326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:16.827153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:19.678672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:22.351239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:24.895908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:27.571993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:30.174475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:32.999988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:35.781223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:38.783096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:41.252529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-01T20:39:43.699943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-06-01T20:39:55.973717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CycleOperation Setting 1Operation Setting 2RULSensor Measure 11Sensor Measure 12Sensor Measure 13Sensor Measure 14Sensor Measure 15Sensor Measure 17Sensor Measure 2Sensor Measure 20Sensor Measure 21Sensor Measure 3Sensor Measure 4Sensor Measure 6Sensor Measure 7Sensor Measure 8Sensor Measure 9UnitNumber
Cycle1.000-0.0060.012-0.7870.615-0.5930.4580.2940.5710.5530.534-0.568-0.5700.5290.6050.108-0.5780.4550.4020.058
Operation Setting 1-0.0061.0000.008-0.0010.0080.000-0.003-0.0030.0050.0030.009-0.006-0.011-0.0070.0090.003-0.008-0.003-0.004-0.020
Operation Setting 20.0120.0081.000-0.0040.012-0.0100.019-0.0190.0140.0130.009-0.011-0.0100.0090.0170.000-0.0170.013-0.018-0.006
RUL-0.787-0.001-0.0041.000-0.7180.693-0.573-0.202-0.666-0.629-0.6290.6530.657-0.606-0.7020.1390.679-0.574-0.3220.058
Sensor Measure 110.6150.0080.012-0.7181.000-0.8320.776-0.0660.7580.6980.717-0.750-0.7500.6700.8120.199-0.8060.7770.0830.025
Sensor Measure 12-0.5930.000-0.0100.693-0.8321.000-0.7770.101-0.745-0.684-0.7060.7340.736-0.660-0.8000.1880.794-0.775-0.046-0.031
Sensor Measure 130.458-0.0030.019-0.5730.776-0.7771.000-0.3280.6870.6180.649-0.676-0.6770.5930.7370.186-0.7540.807-0.1820.046
Sensor Measure 140.294-0.003-0.019-0.202-0.0660.101-0.3281.000-0.0190.032-0.0190.0190.0200.029-0.0350.0820.086-0.3260.886-0.044
Sensor Measure 150.5710.0050.014-0.6660.758-0.7450.687-0.0191.0000.6430.651-0.682-0.6780.6130.7320.173-0.7260.6890.1150.021
Sensor Measure 170.5530.0030.013-0.6290.698-0.6840.6180.0320.6431.0000.605-0.626-0.6320.5750.6780.146-0.6740.6180.1540.014
Sensor Measure 20.5340.0090.009-0.6290.717-0.7060.649-0.0190.6510.6051.000-0.640-0.6430.5760.6930.146-0.6800.6500.1040.014
Sensor Measure 20-0.568-0.006-0.0110.653-0.7500.734-0.6760.019-0.682-0.626-0.6401.0000.668-0.599-0.7270.1620.716-0.677-0.111-0.018
Sensor Measure 21-0.570-0.011-0.0100.657-0.7500.736-0.6770.020-0.678-0.632-0.6430.6681.000-0.610-0.7200.1540.715-0.677-0.114-0.016
Sensor Measure 30.529-0.0070.009-0.6060.670-0.6600.5930.0290.6130.5750.576-0.599-0.6101.0000.6510.125-0.6430.5920.1430.015
Sensor Measure 40.6050.0090.017-0.7020.812-0.8000.737-0.0350.7320.6780.693-0.727-0.7200.6511.0000.170-0.7740.7390.1070.025
Sensor Measure 60.1080.0030.0000.1390.1990.1880.1860.0820.1730.1460.1460.1620.1540.1250.1701.000-0.1610.162-0.0100.026
Sensor Measure 7-0.578-0.008-0.0170.679-0.8060.794-0.7540.086-0.726-0.674-0.6800.7160.715-0.643-0.774-0.1611.000-0.755-0.056-0.033
Sensor Measure 80.455-0.0030.013-0.5740.777-0.7750.807-0.3260.6890.6180.650-0.677-0.6770.5920.7390.162-0.7551.000-0.1790.043
Sensor Measure 90.402-0.004-0.018-0.3220.083-0.046-0.1820.8860.1150.1540.104-0.111-0.1140.1430.107-0.010-0.056-0.1791.000-0.023
UnitNumber0.058-0.020-0.0060.0580.025-0.0310.046-0.0440.0210.0140.014-0.018-0.0160.0150.0250.026-0.0330.043-0.0231.000

Missing values

2024-06-01T20:39:46.419973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-01T20:39:46.858165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

UnitNumberCycleOperation Setting 1Operation Setting 2Operation Setting 3Sensor Measure 1Sensor Measure 2Sensor Measure 3Sensor Measure 4Sensor Measure 5Sensor Measure 6Sensor Measure 7Sensor Measure 8Sensor Measure 9Sensor Measure 10Sensor Measure 11Sensor Measure 12Sensor Measure 13Sensor Measure 14Sensor Measure 15Sensor Measure 16Sensor Measure 17Sensor Measure 18Sensor Measure 19Sensor Measure 20Sensor Measure 21RUL
011-0.0007-0.0004100.0518.67641.821589.701400.6014.6221.61554.362388.069046.191.347.47521.662388.028138.628.41950.033922388100.039.0623.4190192
1120.0019-0.0003100.0518.67642.151591.821403.1414.6221.61553.752388.049044.071.347.49522.282388.078131.498.43180.033922388100.039.0023.4236191
213-0.00430.0003100.0518.67642.351587.991404.2014.6221.61554.262388.089052.941.347.27522.422388.038133.238.41780.033902388100.038.9523.3442190
3140.00070.0000100.0518.67642.351582.791401.8714.6221.61554.452388.119049.481.347.13522.862388.088133.838.36820.033922388100.038.8823.3739189
415-0.0019-0.0002100.0518.67642.371582.851406.2214.6221.61554.002388.069055.151.347.28522.192388.048133.808.42940.033932388100.038.9023.4044188
516-0.0043-0.0001100.0518.67642.101584.471398.3714.6221.61554.672388.029049.681.347.16521.682388.038132.858.41080.033912388100.038.9823.3669187
6170.00100.0001100.0518.67642.481592.321397.7714.6221.61554.342388.029059.131.347.36522.322388.038132.328.39740.033922388100.039.1023.3774186
718-0.00340.0003100.0518.67642.561582.961400.9714.6221.61553.852388.009040.801.347.24522.472388.038131.078.40760.033912388100.038.9723.3106185
8190.00080.0001100.0518.67642.121590.981394.8014.6221.61553.692388.059046.461.347.29521.792388.058125.698.37280.033922388100.039.0523.4066184
9110-0.00330.0001100.0518.67641.711591.241400.4614.6221.61553.592388.059051.701.347.03521.792388.068129.388.42860.033932388100.038.9523.4694183
UnitNumberCycleOperation Setting 1Operation Setting 2Operation Setting 3Sensor Measure 1Sensor Measure 2Sensor Measure 3Sensor Measure 4Sensor Measure 5Sensor Measure 6Sensor Measure 7Sensor Measure 8Sensor Measure 9Sensor Measure 10Sensor Measure 11Sensor Measure 12Sensor Measure 13Sensor Measure 14Sensor Measure 15Sensor Measure 16Sensor Measure 17Sensor Measure 18Sensor Measure 19Sensor Measure 20Sensor Measure 21RUL
20621100191-0.0005-0.0000100.0518.67643.691610.871427.1914.6221.61551.782388.269068.901.348.07519.802388.288143.568.50920.033982388100.038.3923.121810
20622100192-0.00090.0001100.0518.67643.531601.231419.4814.6221.61551.142388.179060.451.348.18520.592388.218143.468.48920.033972388100.038.5623.07709
20623100193-0.00010.0002100.0518.67643.091599.811428.9314.6221.61552.042388.299067.571.348.19520.112388.198142.028.54240.033972388100.038.4723.02308
20624100194-0.00110.0003100.0518.67643.721597.291427.4114.6221.61551.992388.239068.851.348.12519.552388.228139.678.52150.033942388100.038.3823.13247
20625100195-0.0002-0.0001100.0518.67643.411600.041431.9014.6221.61551.422388.239069.691.348.22519.712388.288142.908.55190.033942388100.038.1423.19236
20626100196-0.0004-0.0003100.0518.67643.491597.981428.6314.6221.61551.432388.199065.521.348.07519.492388.268137.608.49560.033972388100.038.4922.97355
20627100197-0.0016-0.0005100.0518.67643.541604.501433.5814.6221.61550.862388.239065.111.348.04519.682388.228136.508.51390.033952388100.038.3023.15944
206281001980.00040.0000100.0518.67643.421602.461428.1814.6221.61550.942388.249065.901.348.09520.012388.248141.058.56460.033982388100.038.4422.93333
20629100199-0.00110.0003100.0518.67643.231605.261426.5314.6221.61550.682388.259073.721.348.39519.672388.238139.298.53890.033952388100.038.2923.06402
20630100200-0.0032-0.0005100.0518.67643.851600.381432.1414.6221.61550.792388.269061.481.348.20519.302388.268137.338.50360.033962388100.038.3723.05221